Abstract
This paper presents a strategy for content-based image retrieval. It is based on a meaningful segmentation procedure that can provide proper distributions for matching via the Earth mover’s distance as a similarity metric. The segmentation procedure is based on a hierarchical watershed-driven algorithm that extracts automatically meaningful regions. In this framework, the proposed robust feature extraction plays a major role along with a novel region weighting for enhancing feature discrimination. Experimental results demonstrate the performance of the proposed strategy.
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References
C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Image segmentation using E-M and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:1026–1038, 2002.
C-S Fuh, S-W Cho, and K. Essig. Hierarchical color image region segmentation for content-based image retrieval system. IEEE Transactions on Image Processing, 9(1):156–162, 2000.
T. Gevers. Image segmentation and similarity of color-texture objects. IEEE Transactions on Multimedia, 4(4):509–516, 2002.
H. Greenspan, G. Dvir, and Y. Rubner. Context-dependent segmentation and matching in image databases. Computer Vision and Image Understanding, 93:86–109, 2004.
J-W. Hsieh and E. Grimson. Spatial template extraction for image retrieval by region matching. IEEE Transactions on Image Processing, 12(11):1404–1415, 2003.
F. Jing, M. Li, H-J Zhang, and B. Zhang. An efficient and effective region-based image retrieval framework. IEEE Transactions on Image Processing, 13(5):699–709, 2004.
T. Kanungo, D. Mount, C.D. Piatko N.S. Netanyahu, R. Silverman, and A.Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):881–892, 2002.
W. Ma and B. Manjunath. NeTra: A toolbox for navigating large image databases. In Proc. IEEE Int’l Conference Image Processing, pages 568–571, 1997.
L. Najman and M. Schmitt. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(12):1163–1173, 1996.
I. Pratikakis. Watershed-driven image segmentation. PhD thesis, Vrije Universiteit Brussel, 1998.
I. Pratikakis, H. Sahli, and J. Cornelis. Hierarchical segmentaion using dynamics of multiscale gradient watersheds. In 11th Scandinavian Conference on Image Analysis (SCIA 99), pages 577–584, 1999.
Y. Rubner and C. Tomasi. Perceptual metrics for image database navigation. Kluwer Academic Publishers, Boston, 2000.
I. Vanhamel, A. Katartzis, and H. Sahli. Hierarchical segmentation via a diffusion scheme in color-texture feature space. In Int. Conf. on Image Processing (ICIP-2003), Barcelona-Spain, 2003.
I. Vanhamel, I. Pratikakis, and H. Sahli. Automatic watershed segmentation of color images. In J. Goutsias, L. Vincent, and D.S. Bloomberg, editors, Mathematical Morphology and its Applications to Image and Signal Processing, Computational imaging and vision, pages 207–214, Parc-Xerox, Palo Alto, CA-USA, 2000. Kluwer Academic Press.
I. Vanhamel, I. Pratikakis, and H. Sahli. Multi-scale gradient watersheds of color images. IEEE Transactions on Image Processing, 12(6):617–626, 2003.
J.Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics-Sensitive integrated Matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947–963, 2001.
Y. Deng and B.S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):800–810, 2001.
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Pratikakis, I., Vanhamel, I., Sahli, H., Gatos, B., Perantonis, S. (2005). Watershed-Driven Region-Based Image Retrieval. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_19
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DOI: https://doi.org/10.1007/1-4020-3443-1_19
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3442-8
Online ISBN: 978-1-4020-3443-5
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